Spectral Metamerism in AI-Mediated Brand Perception: How Large Language Models Collapse Multi-Dimensional Brand Differentiation in Consumer Search

Large language models (LLMs) are rapidly replacing traditional search engines as the primary intermediary in consumer brand evaluation. This paper introduces the LLM-as-observer model within Spectral Brand Theory's eight-dimensional framework and demonstrates that LLMs systematically collapse brand perception to Experiential and Economic dimensions, rendering brands differentiated on narrative, ideological, cultural, or temporal grounds metameric – structurally distinct yet functionally equivalent in AI recommendations. Across nine runs (21, 600+ API calls, 24 models from seven training traditions, 11 languages), we elicit implicit spectral weights via structured prompts using the PRISM-B instrument and compute a Dimensional Collapse Index (DCI). Mean DCI significantly exceeds the uniform baseline (global = 0. 291, p = 0. 017; local = 0. 353, p = 0. 0006; cross-cultural = 0. 357, d = 3. 449). Cross-model convergence is extreme (cosine similarity of spectral profiles = 0. 977). Local brands collapse 25% more severely than global brands (d = 0. 878), consistent with an Economic default mechanism. Western models exhibit lower collapse than non-Western models (p = 0. 0013). Native-language prompting across 11 languages produces no aggregate benefit (46/115 model-pair combinations positive; mean reduction = -0. 005). Geopolitical framing significantly modulates collapse (H12: mean absolute delta = 0. 040, p < 0. 0001). The single exception – Patagonia versus Columbia – shows that legally verifiable ideological commitments survive AI mediation. Providing structured Brand Function specifications reduces collapse by approximately 20%. These findings establish dimensional collapse as a structural feature of text-based AI observers, with implications for search advertising strategy, brand defensibility, and dual-track (human + AI) measurement. Includes paper. yaml (Paper Spec v0. 1. 0) – a machine-readable specification of the paper's claims, assumptions, and dependencies. See https: //github. com/spectralbranding/paper-spec for the standard.